library(class)
library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
library(e1071)
library(magrittr)
library(XML) 
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
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## 
##     intersect, setdiff, setequal, union
library(tidyr)
## 
## Attaching package: 'tidyr'
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## 
##     extract
library(stringi)
library(rvest) 
## Loading required package: xml2
## 
## Attaching package: 'rvest'
## The following object is masked from 'package:XML':
## 
##     xml
library(ggplot2)
library(RCurl) 
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##     complete
library(plotly)
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##     last_plot
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##     filter
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##     layout
library(gridExtra)
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##     combine
library(httr)
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## Attaching package: 'httr'
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## 
##     config
## The following object is masked from 'package:caret':
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##     progress
library(jsonlite)
library(e1071)
library(naniar)
library(scales)
library(gghighlight)
library(stringr)
library(knitr)
library(mice)
## 
## Attaching package: 'mice'
## The following object is masked from 'package:RCurl':
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##     complete
## The following objects are masked from 'package:base':
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##     cbind, rbind
library(Hmisc)
## Loading required package: survival
## 
## Attaching package: 'survival'
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##     cluster
## Loading required package: Formula
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## Attaching package: 'Hmisc'
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##     format.pval, units
library(ggpubr)
library(maps)
library(usmap)
knitr::opts_chunk$set(error = TRUE)
beers=read.csv('/Users/renfengwang/Documents/SMU\ Data\ Science\ Program/Doing\ Data\ Science/Project\ 1/Beers.csv',header=T)
brewery=read.csv('/Users/renfengwang/Documents/SMU\ Data\ Science\ Program/Doing\ Data\ Science/Project\ 1/Breweries.csv',header=T)
head(beers)
##                  Name Beer_ID   ABV IBU Brewery_id
## 1            Pub Beer    1436 0.050  NA        409
## 2         Devil's Cup    2265 0.066  NA        178
## 3 Rise of the Phoenix    2264 0.071  NA        178
## 4            Sinister    2263 0.090  NA        178
## 5       Sex and Candy    2262 0.075  NA        178
## 6        Black Exodus    2261 0.077  NA        178
##                            Style Ounces
## 1            American Pale Lager     12
## 2        American Pale Ale (APA)     12
## 3                   American IPA     12
## 4 American Double / Imperial IPA     12
## 5                   American IPA     12
## 6                  Oatmeal Stout     12
head(brewery)
##   Brew_ID                      Name          City State
## 1       1        NorthGate Brewing    Minneapolis    MN
## 2       2 Against the Grain Brewery    Louisville    KY
## 3       3  Jack's Abby Craft Lagers    Framingham    MA
## 4       4 Mike Hess Brewing Company     San Diego    CA
## 5       5   Fort Point Beer Company San Francisco    CA
## 6       6     COAST Brewing Company    Charleston    SC

Load both CSV files and printed out the head of each data frame to check them visually.

#Question 1
brewery %>% arrange(State) %>% ggplot(aes(x=State),count=Name)+geom_bar()+geom_text(aes(label=..count..),stat='count',vjust=-.5) +
  xlab('States')+ ylab('Brewery Numbers') + ggtitle('Numbers of Brewery by State')  #Bar plot to count the brewery numbers in each state

Plot the numbers of brewery by state. Colorado (47) has the most breweries. California (39), Michigan (32), Oregon (29) and Texas (28) are the next top four states with max breweries.

#Question 2
colnames(beers)[colnames(beers)=='Brewery_id']='Brew_ID'  #Change one of the data frame column names before merging two data sets.
df_beer=merge(beers, brewery, by='Brew_ID', all=T)  #Outer join two data frames.
colnames(df_beer)[colnames(df_beer)=='Name.x']='Beer_Name'
colnames(df_beer)[colnames(df_beer)=='Name.y']='Brewery_Name'

head(df_beer) 
##   Brew_ID     Beer_Name Beer_ID   ABV IBU                               Style
## 1       1  Get Together    2692 0.045  50                        American IPA
## 2       1 Maggie's Leap    2691 0.049  26                  Milk / Sweet Stout
## 3       1    Wall's End    2690 0.048  19                   English Brown Ale
## 4       1       Pumpion    2689 0.060  38                         Pumpkin Ale
## 5       1    Stronghold    2688 0.060  25                     American Porter
## 6       1   Parapet ESB    2687 0.056  47 Extra Special / Strong Bitter (ESB)
##   Ounces       Brewery_Name        City State
## 1     16 NorthGate Brewing  Minneapolis    MN
## 2     16 NorthGate Brewing  Minneapolis    MN
## 3     16 NorthGate Brewing  Minneapolis    MN
## 4     16 NorthGate Brewing  Minneapolis    MN
## 5     16 NorthGate Brewing  Minneapolis    MN
## 6     16 NorthGate Brewing  Minneapolis    MN
tail(df_beer)
##      Brew_ID                 Beer_Name Beer_ID   ABV IBU
## 2405     556             Pilsner Ukiah      98 0.055  NA
## 2406     557  Heinnieweisse Weissebier      52 0.049  NA
## 2407     557           Snapperhead IPA      51 0.068  NA
## 2408     557         Moo Thunder Stout      50 0.049  NA
## 2409     557         Porkslap Pale Ale      49 0.043  NA
## 2410     558 Urban Wilderness Pale Ale      30 0.049  NA
##                        Style Ounces                  Brewery_Name          City
## 2405         German Pilsener     12         Ukiah Brewing Company         Ukiah
## 2406              Hefeweizen     12       Butternuts Beer and Ale Garrattsville
## 2407            American IPA     12       Butternuts Beer and Ale Garrattsville
## 2408      Milk / Sweet Stout     12       Butternuts Beer and Ale Garrattsville
## 2409 American Pale Ale (APA)     12       Butternuts Beer and Ale Garrattsville
## 2410        English Pale Ale     12 Sleeping Lady Brewing Company     Anchorage
##      State
## 2405    CA
## 2406    NY
## 2407    NY
## 2408    NY
## 2409    NY
## 2410    AK

Merged two data frames and printed out head and tail of the new combined data frame.

#Question 3

df_beerall=df_beer %>% replace_with_na_all(condition = ~.x=='')
df_beerall=df_beerall %>% replace_with_na_all(condition = ~.x==' ') # Replace any blank values with NA
gg_miss_var(df_beerall) #Plot to check if any column has missing values

table(is.na(df_beerall$IBU))
## 
## FALSE  TRUE 
##  1405  1005
table(is.na(df_beerall$ABV))
## 
## FALSE  TRUE 
##  2348    62
table(is.na(df_beerall$ABV | df_beerall$IBU)) #Printed out the numbers of missing values in ABV and IBU columns
## 
## FALSE  TRUE 
##  2348    62
imp=mice(df_beerall, method='norm.predict', m=5)
## 
##  iter imp variable
##   1   1  ABV  IBU
##   1   2  ABV  IBU
##   1   3  ABV  IBU
##   1   4  ABV  IBU
##   1   5  ABV  IBU
##   2   1  ABV  IBU
##   2   2  ABV  IBU
##   2   3  ABV  IBU
##   2   4  ABV  IBU
##   2   5  ABV  IBU
##   3   1  ABV  IBU
##   3   2  ABV  IBU
##   3   3  ABV  IBU
##   3   4  ABV  IBU
##   3   5  ABV  IBU
##   4   1  ABV  IBU
##   4   2  ABV  IBU
##   4   3  ABV  IBU
##   4   4  ABV  IBU
##   4   5  ABV  IBU
##   5   1  ABV  IBU
##   5   2  ABV  IBU
##   5   3  ABV  IBU
##   5   4  ABV  IBU
##   5   5  ABV  IBU
## Warning: Number of logged events: 5
beer_imp=complete(imp) #Use linear regression method to replace missing values in ABV and IBU columns.
gg_miss_var(beer_imp)

table(is.na(beer_imp$IBU))
## 
## FALSE 
##  2410
table(is.na(beer_imp$ABV))
## 
## FALSE 
##  2410
table(is.na(beer_imp$Style)) #Check how many missing values in beer style column
## 
## FALSE  TRUE 
##  2405     5

We can see there are missing values in Style, ABV and IBU columns. In ABV column, there are 62 missing values. In IBU column, there are 1005 missing values. In Style column, there are 5 missing values. I replaced the missing values in both ABV and IBU columns by linear regression method. I will replace the missing values in Beer Style column by searching their styles online as there are only 5 missing values.

#Question 4
Median_ABV_IBU=beer_imp %>% arrange(State) %>% group_by(State) %>% summarise(Median_ABV=median(ABV, na.rm=TRUE), Median_IBU=median(IBU,na.rm=TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
Median_ABV_IBU %>% ggplot(aes(x=State, y=Median_ABV,width=.5))+geom_bar(stat='identity') + 
  geom_text(aes(label=percent(Median_ABV, accuracy = 0.01)),vjust=-.5,size=2.5,check_overlap = T) +
  xlab('States') +ylab('Median Alcoholic Content')+ggtitle('Median Alcoholic Content by State')

Median_ABV_IBU %>% ggplot(aes(x=State, y=Median_IBU, width=.5))+geom_bar(stat='identity') + 
  geom_text(aes(label=sprintf("%0.1f", round(Median_IBU, digits = 1))),vjust=-.5,size=2.5,check_overlap = T) +
  xlab('States') +ylab('Median International Bitterness')+ggtitle('Median International Bitterness by State')

Plot median ABV and median IBU by each state. Washington DC and Kentucky have the highest median ABV values which are 6.25%. Maine has the highest median IBU value 61 followed by West Virginia 57.5.

#Question 5
Max_ABV_IBU=beer_imp %>% arrange(State) %>% group_by(State) %>% summarise(Max_ABV=max(ABV, na.rm=TRUE), Max_IBU=max(IBU,na.rm=T))
## `summarise()` ungrouping output (override with `.groups` argument)
Max_ABV_IBU %>% ggplot(aes(x=State, y=Max_ABV,width=.5))+geom_bar(stat='identity') + 
  geom_text(aes(label=percent(Max_ABV, accuracy = 0.01)),vjust=-.5,size=2.5,check_overlap = T) +
  xlab('States') +ylab('Max Alcoholic Content')+ggtitle('Max Alcoholic Content by State')

Max_ABV_IBU %>% ggplot(aes(x=State, y=Max_IBU,width=.5))+geom_bar(stat='identity') + 
  geom_text(aes(label=sprintf("%0.1f", round(Max_IBU, digits = 1))),vjust=-.5,size=2.5,check_overlap = T) +
  xlab('States') +ylab('Max International Bitterness')+ggtitle('Max International Bitterness by State')

The State with the maximum ABV beer is Colorado (12.8%). This is Upslope Brewing Company’s Lee Hill Series Vol.5- Belgian Style Quadrupel Ale. The State with the maximum IBU beer is Oregon (138 IBU). This is Astoria Brewing Company’s Bitter Bitch Imperial IPA.

#Question 6
beer_imp%>% summarise(Mean=mean(ABV, na.rm=TRUE),
                                             Median=median(ABV,na.rm=T),
                                             Min=min(ABV,na.rm=T),
                                             Max=max(ABV,na.rm=T),
                                             SD=sd(ABV,na.rm=T),
                                             N=n())
##         Mean Median   Min   Max         SD    N
## 1 0.05976457  0.057 0.001 0.128 0.01337555 2410
beer_imp %>% filter(!is.na(ABV)) %>% ggplot(aes(x=ABV))+geom_histogram(aes(y=..density..),colour='black',fill='white')+
  geom_density(alpha=.5, fill='#FF6666') #Plot the distribution of ABV
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

The distribution of ABV is right skewed. ABV of beers around 5% has the most counts. There are total 2410 non-missing ABV values in this data set. The maximum ABV is 12.8%, the minimum ABV is .1%, the median ABV is 5.6%. The mean ABV is 5.98% and standard deviation of ABV is 1.35%.

#Question 7
beer_imp %>% filter(!is.na(ABV) &!is.na(IBU)) %>% 
  ggplot(aes(y=ABV, x=IBU))+geom_point(position='jitter')+geom_smooth(method=loess)
## `geom_smooth()` using formula 'y ~ x'

rcorr(beer_imp$ABV, beer_imp$IBU,type='pearson')
##      x    y
## x 1.00 0.76
## y 0.76 1.00
## 
## n= 2410 
## 
## 
## P
##   x  y 
## x     0
## y  0
ggscatter(beer_imp,x='IBU', y='ABV',add='reg.line',conf.int=T,cor.coef = T,cor.method='pearson')
## `geom_smooth()` using formula 'y ~ x'

#Checked the linear correlation between ABV and IBU

Most beers with lower IBU (less than 50) have ABV values around 5%. When IBU value increases, ABV values spreads out. But most beers with IBU values above 50, their ABV values spread out within the region between 5% and 10%. We can find out that some dots have clearly linear regression as those are missing values which were replaced by linear regression method. Thus, the correlation coefficient is 0.76 which is probably larger than if I remove all missing values.

#Question 8  KNN
beer_imp %>% filter(is.na(beer_imp$Style)) #Print out all rows contains missing values in beer style column.
##   Brew_ID                      Beer_Name Beer_ID        ABV      IBU Style
## 1      30                Special Release    2210 0.06309148 46.41795  <NA>
## 2      67                  OktoberFiesta    2527 0.05300000 27.00000  <NA>
## 3     161 Kilt Lifter Scottish-Style Ale    1635 0.06000000 21.00000  <NA>
## 4     167                   The CROWLER™    1796 0.07715341 61.58128  <NA>
## 5     167           CAN'D AID Foundation    1790 0.05849041 41.26661  <NA>
##   Ounces               Brewery_Name         City State
## 1     16        Cedar Creek Brewery Seven Points    TX
## 2     12   Freetail Brewing Company  San Antonio    TX
## 3     12 Four Peaks Brewing Company        Tempe    AZ
## 4     32        Oskar Blues Brewery     Longmont    CO
## 5     12        Oskar Blues Brewery     Longmont    CO
beer_imp$Style[beer_imp$Beer_ID=='2210']='Red Ale - American Amber/Red'
beer_imp$Style[beer_imp$Beer_ID=='2527']='Lager - Märzen/Oktoberfest'
beer_imp$Style[beer_imp$Beer_ID=='1635']='Scottish Ale'
beer_imp$Style[beer_imp$Beer_ID=='1796']='IPA - AMERICAN'
beer_imp$Style[beer_imp$Beer_ID=='1790']='FRUITED ALE'
#Replaced five missing values in beer style column to real data found online.   
           
df_beer_IPA=beer_imp %>% filter(!is.na(ABV) &!is.na(IBU)) %>% 
  filter(str_detect(Style, regex(str_c('\\b','IPA','\\b',sep=''), ignore_case = T))) #Find all IPA style beer

df_beer_IPA$Style=as.factor('IPA') #Replace all IPA style beer with style name 'IPA'

df_beer_Ale=beer_imp %>% filter(!is.na(ABV) &!is.na(IBU)) %>% 
  filter(str_detect(Style, regex(str_c('\\b','Ale','\\b',sep=''), ignore_case = T))) #Find all Ale style beer

df_beer_Ale$Style=as.factor('Ale') #Replace all Ale style beer with style name 'Ale'

df_beer_test=rbind(df_beer_IPA, df_beer_Ale) #Combine these two data frames

df_beer_test %>% ggplot(aes(x=ABV, y=IBU)) +geom_point(aes(colour=Style)) #Scatter plot for all IPA and Ale styles beer

iterations = 500
numks = 30
splitPerc = .7
masterAcc = matrix(nrow = iterations, ncol = numks)
set.seed(6)
trainIndices = sample(1:dim(df_beer_test)[1],round(splitPerc * dim(df_beer_test)[1])) #Shuffle the training and testing groups
beer_train = df_beer_test[trainIndices,]
beer_test = df_beer_test[-trainIndices,]
classifications=knn(beer_train[,c(4,5)],beer_test[,c(4,5)],beer_train$Style, prob = TRUE, k = 5) 
CM = confusionMatrix(table(classifications,beer_test$Style))
classifications  #Use KNN method to predict beer style by its ABV and IBU values
##   [1] IPA IPA IPA Ale Ale IPA IPA IPA IPA IPA Ale IPA IPA IPA IPA Ale IPA IPA
##  [19] Ale IPA Ale Ale IPA IPA Ale Ale Ale IPA IPA IPA IPA Ale IPA IPA Ale IPA
##  [37] IPA IPA Ale IPA IPA IPA IPA IPA IPA IPA IPA IPA Ale IPA IPA IPA Ale IPA
##  [55] IPA IPA IPA IPA IPA IPA IPA IPA Ale IPA IPA IPA IPA IPA Ale IPA Ale IPA
##  [73] IPA IPA Ale IPA IPA Ale IPA Ale Ale IPA IPA IPA Ale IPA IPA IPA IPA IPA
##  [91] Ale Ale IPA Ale Ale IPA Ale Ale Ale IPA IPA IPA Ale IPA Ale IPA Ale Ale
## [109] IPA IPA Ale IPA IPA Ale Ale IPA IPA IPA IPA IPA IPA IPA IPA IPA Ale Ale
## [127] Ale IPA IPA Ale Ale IPA IPA Ale IPA IPA IPA Ale Ale IPA Ale IPA Ale Ale
## [145] Ale Ale Ale IPA IPA Ale Ale IPA IPA IPA IPA Ale Ale Ale Ale Ale Ale Ale
## [163] Ale Ale IPA IPA IPA Ale IPA Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale
## [181] Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale
## [199] Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale
## [217] Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale Ale Ale IPA
## [235] Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale
## [253] Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale Ale IPA Ale Ale
## [271] Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale IPA IPA Ale Ale
## [289] Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale IPA IPA
## [307] IPA Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale
## [325] Ale Ale Ale Ale IPA IPA Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale
## [343] Ale IPA Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale
## [361] Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale Ale Ale
## [379] Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale
## [397] IPA IPA Ale Ale Ale IPA Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale
## [415] IPA Ale Ale IPA IPA Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA
## [433] Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale Ale Ale Ale Ale Ale Ale
## [451] Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale Ale IPA Ale Ale Ale
## attr(,"prob")
##   [1] 0.6000000 1.0000000 0.6000000 0.6000000 0.6000000 0.5000000 1.0000000
##   [8] 0.8888889 1.0000000 0.8000000 0.8235294 0.6000000 1.0000000 1.0000000
##  [15] 1.0000000 0.6000000 0.6000000 1.0000000 0.8000000 0.8000000 0.6666667
##  [22] 1.0000000 1.0000000 0.8888889 0.6000000 0.8000000 0.8000000 0.6000000
##  [29] 0.8000000 0.8333333 0.8000000 0.6000000 0.8571429 0.8571429 0.8000000
##  [36] 1.0000000 1.0000000 0.8000000 0.6000000 0.6000000 0.8000000 0.8571429
##  [43] 0.9000000 0.6000000 0.8000000 1.0000000 1.0000000 1.0000000 1.0000000
##  [50] 0.8888889 1.0000000 0.8000000 0.8333333 1.0000000 0.8571429 1.0000000
##  [57] 1.0000000 1.0000000 0.8000000 1.0000000 1.0000000 1.0000000 0.5000000
##  [64] 1.0000000 0.8333333 1.0000000 0.6666667 1.0000000 0.8000000 0.6000000
##  [71] 0.8000000 0.8000000 0.6000000 0.6000000 1.0000000 1.0000000 0.6000000
##  [78] 0.6000000 1.0000000 1.0000000 0.6666667 0.6000000 0.6000000 1.0000000
##  [85] 0.6000000 0.6000000 0.8000000 0.8571429 0.8571429 0.8888889 0.8000000
##  [92] 0.8000000 1.0000000 0.8000000 0.7142857 1.0000000 0.8000000 0.8000000
##  [99] 0.8000000 0.6000000 1.0000000 0.8333333 0.8235294 0.6000000 1.0000000
## [106] 0.8571429 0.8333333 0.8000000 0.8000000 0.6000000 0.6000000 1.0000000
## [113] 0.6000000 0.8333333 0.8235294 0.6000000 1.0000000 0.8571429 0.8571429
## [120] 0.8571429 0.9000000 0.8888889 1.0000000 1.0000000 1.0000000 0.8000000
## [127] 0.6000000 1.0000000 0.8750000 0.6000000 0.8000000 0.8000000 0.9000000
## [134] 0.8000000 1.0000000 1.0000000 0.6000000 0.8000000 0.6000000 0.8000000
## [141] 0.8000000 0.6666667 0.8000000 0.8000000 0.8000000 0.8000000 0.6000000
## [148] 0.8333333 1.0000000 0.6000000 0.8235294 0.6000000 0.8333333 1.0000000
## [155] 1.0000000 0.6000000 1.0000000 0.6000000 0.6666667 0.6000000 0.8000000
## [162] 1.0000000 1.0000000 1.0000000 0.6000000 0.6000000 1.0000000 0.8000000
## [169] 0.6000000 1.0000000 0.8000000 0.8000000 1.0000000 0.7777778 1.0000000
## [176] 1.0000000 0.8000000 0.8000000 0.6000000 0.6000000 1.0000000 0.8333333
## [183] 0.7142857 1.0000000 1.0000000 0.6000000 0.8000000 0.6000000 1.0000000
## [190] 1.0000000 1.0000000 0.6666667 0.6000000 1.0000000 1.0000000 1.0000000
## [197] 0.6000000 0.8000000 1.0000000 0.6000000 0.6000000 1.0000000 1.0000000
## [204] 0.8000000 1.0000000 0.6000000 0.6000000 1.0000000 0.8333333 0.8000000
## [211] 1.0000000 0.6000000 1.0000000 1.0000000 0.8000000 0.6000000 0.6000000
## [218] 1.0000000 0.8000000 0.8333333 1.0000000 1.0000000 1.0000000 1.0000000
## [225] 0.6250000 0.8000000 1.0000000 1.0000000 1.0000000 0.8000000 0.6000000
## [232] 1.0000000 0.6000000 0.7500000 0.8000000 1.0000000 0.6000000 1.0000000
## [239] 1.0000000 1.0000000 0.7500000 0.6250000 0.6250000 1.0000000 0.8000000
## [246] 1.0000000 0.6000000 1.0000000 0.8235294 0.8000000 0.8000000 1.0000000
## [253] 0.6000000 0.6000000 1.0000000 0.8000000 0.8000000 1.0000000 1.0000000
## [260] 1.0000000 1.0000000 1.0000000 1.0000000 0.6000000 1.0000000 1.0000000
## [267] 0.8000000 0.6000000 1.0000000 0.8000000 1.0000000 0.8000000 0.8000000
## [274] 0.8000000 0.6000000 0.8000000 0.6000000 1.0000000 0.8000000 0.6000000
## [281] 1.0000000 1.0000000 1.0000000 0.8235294 0.6000000 1.0000000 1.0000000
## [288] 1.0000000 0.6000000 0.8000000 0.8000000 1.0000000 0.8000000 0.8000000
## [295] 1.0000000 0.8000000 1.0000000 0.6000000 1.0000000 1.0000000 0.6000000
## [302] 0.8000000 0.8000000 1.0000000 0.8333333 0.8000000 0.6000000 1.0000000
## [309] 1.0000000 0.6000000 0.7777778 0.8000000 0.8000000 1.0000000 1.0000000
## [316] 1.0000000 1.0000000 0.8000000 1.0000000 0.6000000 0.6666667 1.0000000
## [323] 1.0000000 0.6000000 1.0000000 0.8000000 1.0000000 0.8000000 1.0000000
## [330] 0.6000000 1.0000000 0.8000000 1.0000000 1.0000000 0.6666667 1.0000000
## [337] 0.6000000 1.0000000 0.8000000 0.7777778 1.0000000 1.0000000 1.0000000
## [344] 0.8333333 1.0000000 0.6000000 1.0000000 1.0000000 1.0000000 0.6000000
## [351] 0.8333333 0.6250000 1.0000000 1.0000000 1.0000000 1.0000000 0.6000000
## [358] 1.0000000 0.6000000 0.6250000 1.0000000 0.8000000 1.0000000 1.0000000
## [365] 1.0000000 0.6000000 0.6666667 1.0000000 1.0000000 0.7777778 1.0000000
## [372] 1.0000000 0.6000000 1.0000000 0.7777778 0.6000000 1.0000000 1.0000000
## [379] 1.0000000 0.6000000 0.6000000 1.0000000 0.6000000 1.0000000 0.8000000
## [386] 1.0000000 0.8000000 0.6000000 1.0000000 0.6250000 1.0000000 1.0000000
## [393] 1.0000000 0.6666667 1.0000000 0.6000000 0.6000000 0.6000000 0.5555556
## [400] 1.0000000 0.8000000 0.8888889 1.0000000 1.0000000 1.0000000 1.0000000
## [407] 0.8000000 1.0000000 0.8000000 0.6250000 0.6000000 0.8000000 0.8000000
## [414] 0.6000000 0.8000000 0.6666667 1.0000000 0.8333333 0.6000000 1.0000000
## [421] 0.6000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6000000 1.0000000
## [428] 1.0000000 0.8000000 0.6666667 1.0000000 0.8000000 0.6666667 1.0000000
## [435] 1.0000000 0.8000000 1.0000000 1.0000000 0.6000000 1.0000000 1.0000000
## [442] 0.6666667 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.8235294
## [449] 0.8571429 1.0000000 0.8571429 1.0000000 1.0000000 1.0000000 0.8000000
## [456] 0.8000000 1.0000000 0.8000000 0.8000000 1.0000000 0.8000000 0.8333333
## [463] 1.0000000 1.0000000 0.8000000
## Levels: IPA Ale
CM 
## Confusion Matrix and Statistics
## 
##                
## classifications IPA Ale
##             IPA 102  37
##             Ale  54 272
##                                           
##                Accuracy : 0.8043          
##                  95% CI : (0.7653, 0.8394)
##     No Information Rate : 0.6645          
##     P-Value [Acc > NIR] : 1.738e-11       
##                                           
##                   Kappa : 0.5489          
##                                           
##  Mcnemar's Test P-Value : 0.09349         
##                                           
##             Sensitivity : 0.6538          
##             Specificity : 0.8803          
##          Pos Pred Value : 0.7338          
##          Neg Pred Value : 0.8344          
##              Prevalence : 0.3355          
##          Detection Rate : 0.2194          
##    Detection Prevalence : 0.2989          
##       Balanced Accuracy : 0.7671          
##                                           
##        'Positive' Class : IPA             
##

I replaced five missing values in beer style column missing by real beer style I got online. I randomly assigned 70% of original data to training data and 30% of the original data as testing data when seed value is set to 6. We can tell when k=5, the accuracy is about 79%.

for(j in 1:iterations)
{
  accs = data.frame(accuracy = numeric(30), k = numeric(30))
  trainIndices = sample(1:dim(df_beer_test)[1],round(splitPerc * dim(df_beer_test)[1]))
  beer_train = df_beer_test[trainIndices,]
  beer_test = df_beer_test[-trainIndices,]
  for(i in 1:numks)
  {
    classifications = knn(beer_train[,c(4,5)],beer_test[,c(4,5)],beer_train$Style, prob = TRUE, k = i)
    table(classifications,beer_test$Style)
    CM = confusionMatrix(table(classifications,beer_test$Style))
    masterAcc[j,i] = CM$overall[1]
  }
}
MeanAcc = colMeans(masterAcc)
p=ggplot(mapping=aes(x=seq(1,numks,1), y=MeanAcc))+geom_line()
ggplotly(p)  #Shuffle the training and testing groups 500 times and loop the k value from 1 to 30

I shuffled the training and testing data 500 times by 70%/30% split. And assigned integer k value from 1 to 30. From the plot, we can tell when k=5, it gives us the highest accuracy 85% which means we can predict the beer is either India Pale Ales or any other types of Ale by knowing its ABV and IBU values with 85% accuracy when sets nearest neighbor numbers equals to 5

#Naive Bayes--------------

iterations = 500
masterAcc = matrix(nrow = iterations)
masterSen = matrix(nrow = iterations)
masterSpec = matrix(nrow = iterations)
splitPerc = .7 
for(j in 1:iterations)
{
  
  trainIndices = sample(1:dim(df_beer_test)[1],round(splitPerc * dim(df_beer_test)[1]))
  beer_train = df_beer_test[trainIndices,]
  beer_test = df_beer_test[-trainIndices,]
  model = naiveBayes(beer_train[,c(4,5)],as.factor(beer_train$Style),laplace = 1)
  table(predict(model,beer_test[,c(4,5)]),as.factor(beer_test$Style))
  CM = confusionMatrix(table(predict(model,beer_test[,c(4,5)]),as.factor(beer_test$Style)))
  masterAcc[j] = CM$overall[1]
  masterSen[j] = CM$byClass[1]
  masterSpec[j] = CM$byClass[2]
}
MeanAcc = colMeans(masterAcc)
MeanSen = colMeans(masterSen)
MeanSpec = colMeans(masterSpec)
MeanAcc
## [1] 0.7954065
MeanSen
## [1] 0.6750198
MeanSpec  #Use Naive Bayes model to predict the mean accuracy by shufftling training and testing groups 500 times
## [1] 0.8657228

Now I chose to use Naive Bayes model as I want to compare the accuracy with KNN model. I shuffled the training and testing data 500 times by 70%/30% split like I did in KNN model. The mean accuracy of Naive Bayes is 84% which is similar to what we got from KNN.

#Question 9 ABV and IBU average by state
Mean_ABV_IBU=beer_imp %>% group_by(State) %>% summarise(Mean_ABV=mean(ABV, na.rm=TRUE), Mean_IBU=mean(IBU,na.rm=TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
colnames(Mean_ABV_IBU)[colnames(Mean_ABV_IBU)=='State']='abbr'
statedata= statepop[,c(2,3)]
statedata$abbr=as.factor(statedata$abbr)
Mean_ABV_IBU$abbr=as.factor(Mean_ABV_IBU$abbr)
statedata1=merge(statedata,Mean_ABV_IBU,by='abbr',all.x=T)
statedata2=cbind(statedata1,Mean_ABV_IBU)
statedata3=statedata2[,c(1,2,6,7)]
colnames(statedata3)[colnames(statedata3)=='full']='state'  #Change the column name to state before plotting.

plot_usmap(data = statedata3, values = "Mean_ABV", color = "red") + 
  scale_fill_continuous(
    low = "white", high = "red", name = "Mean_ABV", label = scales::comma
  ) + theme(legend.position = "right") + ggtitle('Mean ABV by State')  #Plot average ABV by state

plot_usmap(data = statedata3, values = "Mean_IBU", color = "black") + 
  scale_fill_continuous(
    low = "white", high = "blue", name = "Mean_IBU", label = scales::comma
  ) + theme(legend.position = "right") + ggtitle('Mean IBU by State') #Plot average IBU by state

If the states’ colors are close to white, it means lower mean IBU and ABV.

#Back to Question 7
df_beer_study=rbind(df_beer_sort, df_beer_Ale)
## Error in rbind(df_beer_sort, df_beer_Ale): object 'df_beer_sort' not found
df_beer_study %>% ggplot(aes(y=ABV, x=IBU))+geom_point(position='jitter')+geom_smooth() +facet_wrap(~Style)
## Error in eval(lhs, parent, parent): object 'df_beer_study' not found

Now we came back to Question 7 by checking four different styles of beer. We can see, majority of lager style beer has low IBU and its ABV values are around 5%. No ABV values of lager beer are above 7.5%. ABV values of Indian Pale Ale style seems like increases when IBU increases, but their ABV values don’t pass 10% and majority of their ABV values are between 5% to 10%. The other types of Ale beer have low IBU values which most of them are below 50. Also, most of their ABV values are between 3.75% to 10%. The data size of stout style beer is small, but we can roughly tell their ABV increases when IBU increases.